Glucocorticoids <i>vs</i> glucocorticoids plus cyclophosphamide in eosinophilic granulomatosis with polyangiitis without poor-prognosis factors: a target trial emulation study
Bibliographic record
Abstract
OBJECTIVES: Current recommendations suggest treating eosinophilic granulomatosis with polyangiitis (EGPA) without severe manifestations with glucocorticoids (GCs) and EGPA with severe manifestations with GCs plus cyclophosphamide (CYC) regardless of poor-prognostic factors. However, GCs plus CYC and GCs alone have never been compared in EGPA without poor-prognosis factors assessed by the 1996 Five Factor Score (FFS). We aimed to compare the efficacy of GCs plus CYC vs GCs alone for the treatment of EGPA without poor-prognosis, including among patients with severe manifestations. METHODS: We emulated a target trial using observational data from a European multicentre retrospective study. We included patients with (i) newly diagnosed EGPA, (ii) a FFS = 0 at diagnosis and (iii) treated with GCs or GCs plus CYC. Primary outcome was overall relapse at 12 months. Inverse probability of treatment weighting-based analysis was used to adjust for potential confounding factors. In a subgroup analysis, we focused on patients with severe manifestations not included in the FFS. RESULTS: A total of 250 patients were included: 177 treated with GCs alone and 73 with GCs plus CYC. After adjustment, no reduction in the risk of overall relapse was observed between the two treatment groups. Similar results were observed in the subgroup of patients with severe manifestations. CONCLUSION: This study shows that the adjunction of CYC to GCs does not reduce the risk of relapse in patients with EGPA and no poor-prognosis factors. It supports current guidelines in patients without severe manifestations but challenges the need of CYC adjunction in patients with severe manifestations but no poor-prognosis factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".